## Cross-validation, the Jackknife, and the Bootstrap: Excess Error Estimation in Forward Logistic Regression |

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### Contents

Introduction l | |

The Asymptotic Relationship Between | iii |

Checking Regularity Conditions | 20 |

2 other sections not shown

### Common terms and phrases

20 experiments 400 bootstrap replications 400 experiments albumin apparent error approximation Bernoulli with probability bilirubin bootstrap estimate bootstrap models bound in Lemma Chapter confidence interval converges counts the number covariates cross boot cross-validation estimate Define discriminant analysis Display 2 shows Displays l4 Doctor of Philosophy Efron empirical distribution error rate estimate of expected expected excess error experiments of Simulation forward logistic regression Gregory Gregory's rule histogram jackknife estimate linear model logistic model matrix norms mean square error observations perform predicting death prediction rule based probability of death Proof of Lemma q(F F regularity conditions resampling rescaled apparent 0000 rescaled rescaled apparent results of 400 root mean square sample sizes Scatterplots SD(r Simulation l.l t t t.t t t t.t t t t.t terminal node training sample true error true excess vector